Bioinformatics has evolved as a great tool for molecular biologists. There are various tools available for helping in reducing the time required to analyze biological materials be it DNA, RNA, Proteins etc. I wish to list here few of the commonly used tools. Please send me suggestions to improve the content. If you like or dislike something, let me know, your inputs matters. contact me: drsanjivk[at]gmail[dot]com

We have carried out weighted gene co-expression network analysis of Mycobacterium tuberculosis
to gain insights into gene expression architecture during log phase
growth. The differentially expressed genes between at least one pair of
11 different M. tuberculosis strains as source of biological
variability were used for co-expression network analysis. This data
included genes with highest coefficient of variation in expression. Five
distinct modules were identified using topological overlap based
clustering. All the modules together showed significant enrichment in
biological processes: fatty acid biosynthesis, cell membrane,
intracellular membrane bound organelle, DNA replication, Quinone
biosynthesis, cell shape and peptidoglycan biosynthesis, ribosome and
structural constituents of ribosome and transposition. We then extracted
the co-expressed connections which were supported either by
transcriptional regulatory network or STRING database or high edge
weight of topological overlap. The genes trpC, nadC, pitA, Rv3404c, atpA, pknA, Rv0996, purB, Rv2106 and Rv0796 emerged as top hub genes. After overlaying this network on the iNJ661 metabolic network, the reactions catalyzed by 15 highly connected metabolic genes were knocked down in silico
and evaluated by Flux Balance Analysis. The results showed that in 12
out of 15 cases, in 11 more than 50% of reactions catalyzed by genes
connected through co-expressed connections also had altered fluxes. The
modules ‘Turquoise’, ‘Blue’ and ‘Red’ also
showed enrichment in essential genes. We could map 152 of the previously
known or proposed drug targets in these modules and identified 15 new
potential drug targets based on their high degree of co-expressed
connections and strong correlation with module eigengenes.